GLOSSARY
Embedding
An embedding represents items such as texts as numerical sequences, making model-relevant properties and similarities comparable; proximity does not mean factual equivalence.
Similarity can make information easier to find
A customer asks about faster delivery while internal documents discuss lead times and capacity planning. An embedding-based search may find useful material even when the wording differs. This can improve access to existing knowledge. Its economic effect depends on whether relevant documents are found faster and actually answer the question. Similarity alone is insufficient: two contract clauses can look linguistically close while creating opposite obligations. Names, figures and negations also deserve explicit checking.
Assess the match against its source
A vector database can store and search these representations. A dependable process needs to retain the connection between a result and its original text, version and permissions. Embeddings replace neither the content nor its professional assessment. Tests for a RAG application should therefore include misleading near matches: old price lists, similarly named product variants or clauses containing exceptions. Assess whether retrieval reliably supplies the required information under these conditions. Only then can the organisation judge whether it offers a practical advantage over a simpler search method.